{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 一、项目背景介绍\n",
    ">针对诸多业务场景下的海量债券图表数据处理效率低下的问题，本文基于深度学习技术设计应用于债券图表、图文字光\n",
    "学符号识别 OCR（Optical Character Recognition）解决方案。本文通过基于PaddleOCR开发套件所设计的债券图表 OCR 的检测和识别方\n",
    "案，能较好地识别图像中的文字如：身份证、校园卡、银行卡、财务报表等，实现不同实际业务场景中的需求，在一定程度上提高日常应用业务场景的智能化处理能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 涉及工具：\n",
    "### 1. PaddlePaddle原生框架：\n",
    "百度出品的深度学习平台飞桨（PaddlePaddle）是主流深度学习框架中一款完全国产化的产品，与Google TensorFlow、Facebook Pytorch齐名。2016 年飞桨正式开源，是国内首个全面开源开放、技术领先、功能完备的产业级深度学习平台。相比国内其他平台，飞桨是一个功能完整的深度学习平台，也是唯一成熟稳定、具备大规模推广条件的深度学习平台。\n",
    "### 2. PaddleOCR工具库：\n",
    "PaddleOCR是一个与OCR相关的开源项目，不仅支持超轻量级中文OCR预测模型，总模型仅8.6M（单模型支持中英文数字组合识别、竖排文本识别、长文本识别，其中检测模型DB（4.1M）+识别模型CRNN（4.5M）），而且提供多种文本检测训练算法（EAST、DB）和多种文本识别训练算法（Rosetta、CRNN、STAR-Net、RARE）。\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/3cb25eba88674aca819370a36f5ea70d3e15a7f330d34acc96733cf9ab8b5be9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 设计方案\n",
    "![](https://bochuang.web.cloudendpoint.cn/img/lunwen1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 效果演示：\n",
    "![](https://bochuang.web.cloudendpoint.cn/img/lunwen2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 二、数据介绍\n",
    ">模型训练集为ICDAR2019-LSVT（Large-scale Street View Text with Partial La￾beling）中文街景图像数据集，其中包含 5W（2W 测试 +3W\n",
    "训练）全标注数据（文本坐标 + 文本内容）使用矩形框标注文本行；测试数据集通过 Python 爬虫随机爬取 + 自行收集的身份证、银行卡和财务报表等数据和随机选取训练集中 20%作为测试数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR/train_data/train_img\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f92dacd1790>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%cd PaddleOCR/train_data/train_img/\r\n",
    "import os\r\n",
    "import cv2\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\r\n",
    "img = cv2.imread('img_path_615.jpg')\r\n",
    "plt.imshow(img[:, :, [2, 1, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 三、开发套件介绍\n",
    "# **PaddleOCR**\n",
    "## 简介\n",
    "[PaddleOCR](https://gitee.com/paddlepaddle/PaddleOCR/)旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。\n",
    "## 特性\n",
    "- 超轻量级中文OCR模型，总模型仅8.6M\n",
    "    - 单模型支持中英文数字组合识别、竖排文本识别、长文本识别\n",
    "    - 检测模型DB（4.1M）+识别模型CRNN（4.5M）\n",
    "- 实用通用中文OCR模型\n",
    "- 多种预测推理部署方案，包括服务部署和端侧部署\n",
    "- 多种文本检测训练算法，EAST、DB、SAST\n",
    "- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE、SRN\n",
    "- 可运行于Linux、Windows、MacOS等多种系统\n",
    "## 效果展示\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/b7f60613e19f4f089b28bc58ad28ba4ef3df01f539ad48898483a3e19303e484)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### PaddleOCR依赖库安装\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting shapely\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9d/4d/4b0d86ed737acb29c5e627a91449470a9fb914f32640db3f1cb7ba5bc19e/Shapely-1.8.1.post1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.0 MB)\n",
      "     |████████████████████████████████| 2.0 MB 14.9 MB/s            \n",
      "\u001b[?25hCollecting imgaug\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/66/b1/af3142c4a85cba6da9f4ebb5ff4e21e2616309552caca5e8acefe9840622/imgaug-0.4.0-py2.py3-none-any.whl (948 kB)\n",
      "     |████████████████████████████████| 948 kB 5.0 MB/s            \n",
      "\u001b[?25hCollecting pyclipper\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c5/fa/2c294127e4f88967149a68ad5b3e43636e94e3721109572f8f17ab15b772/pyclipper-1.3.0.post2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (603 kB)\n",
      "     |████████████████████████████████| 603 kB 77.8 MB/s            \n",
      "\u001b[?25hCollecting lmdb\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4d/cf/3230b1c9b0bec406abb85a9332ba5805bdd03a1d24025c6bbcfb8ed71539/lmdb-1.3.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (298 kB)\n",
      "     |████████████████████████████████| 298 kB 10.2 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (4.36.1)\n",
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      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/34/a3/403dbaef909fee9f9f6a8eaff51d44085a14e5bb1a1ff7257117d744986a/opencv_python-4.2.0.32-cp37-cp37m-manylinux1_x86_64.whl (28.2 MB)\n",
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      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d2/d9/d16d4cbb4840e0fb3bd329b49184d240b82b649e1bd579489394fbc85c81/scikit_image-0.19.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (13.5 MB)\n",
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      "Collecting PyWavelets>=1.1.1\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a1/9c/564511b6e1c4e1d835ed2d146670436036960d09339a8fa2921fe42dad08/PyWavelets-1.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (6.1 MB)\n",
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      "Collecting tifffile>=2019.7.26\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d8/38/85ae5ed77598ca90558c17a2f79ddaba33173b31cf8d8f545d34d9134f0d/tifffile-2021.11.2-py3-none-any.whl (178 kB)\n",
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      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->imgaug->-r requirements.txt (line 2)) (2.8.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->imgaug->-r requirements.txt (line 2)) (3.0.7)\n",
      "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->imgaug->-r requirements.txt (line 2)) (56.2.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from networkx>=2.2->scikit-image>=0.14.2->imgaug->-r requirements.txt (line 2)) (4.4.2)\n",
      "Installing collected packages: tifffile, PyWavelets, shapely, scikit-image, opencv-python, pyclipper, lmdb, imgaug\n",
      "  Attempting uninstall: opencv-python\n",
      "    Found existing installation: opencv-python 4.1.1.26\n",
      "    Uninstalling opencv-python-4.1.1.26:\n",
      "      Successfully uninstalled opencv-python-4.1.1.26\n",
      "Successfully installed PyWavelets-1.2.0 imgaug-0.4.0 lmdb-1.3.0 opencv-python-4.2.0.32 pyclipper-1.3.0.post2 scikit-image-0.19.2 shapely-1.8.1.post1 tifffile-2021.11.2\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR\r\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR/\r\n",
    "#!pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  def convert_to_list(value, n, name, dtype=np.int):\n",
      "2021-05-11 10:59:13,714 - INFO - {'Global': {'debug': False, 'algorithm': 'CLS', 'use_gpu': False, 'epoch_num': 100, 'log_smooth_window': 20, 'print_batch_step': 100, 'save_model_dir': 'output/cls_mv3', 'save_epoch_step': 3, 'eval_batch_step': 500, 'train_batch_size_per_card': 512, 'test_batch_size_per_card': 512, 'image_shape': [3, 48, 192], 'label_list': ['0', '180'], 'distort': True, 'reader_yml': './configs/cls/cls_reader.yml', 'pretrain_weights': None, 'checkpoints': './inference/ch_cls/best_accuracy', 'save_inference_dir': None, 'infer_img': 'doc/imgs_words/en/fgfh.jpg'}, 'Architecture': {'function': 'ppocr.modeling.architectures.cls_model,ClsModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3', 'scale': 0.35, 'model_name': 'small'}, 'Head': {'function': 'ppocr.modeling.heads.cls_head,ClsHead', 'class_dim': 2}, 'Loss': {'function': 'ppocr.modeling.losses.cls_loss,ClsLoss'}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay', 'step_each_epoch': 1169, 'total_epoch': 100}}, 'TrainReader': {'reader_function': 'ppocr.data.cls.dataset_traversal,SimpleReader', 'num_workers': 8, 'img_set_dir': './train_data/cls', 'label_file_path': './train_data/cls/train.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.cls.dataset_traversal,SimpleReader', 'img_set_dir': './train_data/cls', 'label_file_path': './train_data/cls/test.txt'}, 'TestReader': {'reader_function': 'ppocr.data.cls.dataset_traversal,SimpleReader'}}\n",
      "2021-05-11 10:59:13,981 - INFO - Finish initing model from ./inference/ch_cls/best_accuracy\n",
      "2021-05-11 10:59:13,982 - INFO - Distort operation can only support in GPU.Distort will be set to False.\n",
      "2021-05-11 10:59:13,982 - INFO - infer_img:doc/imgs_words/en/fgfh.jpg\n",
      "2021-05-11 10:59:14,053 - INFO - \t scores: [[0.72053534 0.27946466]]\n",
      "2021-05-11 10:59:14,054 - INFO - \t label: [0]\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./inference/ch_cls/best_accuracy Global.infer_img=doc/imgs_words/en/fgfh.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/data\n"
     ]
    }
   ],
   "source": [
    "%cd ~/data\r\n",
    "\r\n",
    "!tar -zxf data8429/train_images.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 下载预训练模型和解压训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR/output\n",
      "--2021-02-15 13:04:27--  https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar\n",
      "Resolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.229, 182.61.200.195, 2409:8c00:6c21:10ad:0:ff:b00e:67d\n",
      "Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.229|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 297577776 (284M) [application/x-tar]\n",
      "Saving to: ‘./rec_CRNN/ch_ppocr_server_v1.1_rec_pre.tar.1’\n",
      "\n",
      "ch_ppocr_server_v1. 100%[===================>] 283.79M  30.3MB/s    in 8.5s    \n",
      "\n",
      "2021-02-15 13:04:35 (33.3 MB/s) - ‘./rec_CRNN/ch_ppocr_server_v1.1_rec_pre.tar.1’ saved [297577776/297577776]\n",
      "\n",
      "/home/aistudio/PaddleOCR/output/rec_CRNN\n"
     ]
    }
   ],
   "source": [
    "# 下载预训练模型\n",
    "%cd ~/PaddleOCR/output/\n",
    "!wget -P ./rec_CRNN/ https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar\n",
    "\n",
    "# 解压预训练模型\n",
    "%cd rec_CRNN\n",
    "!tar -xf ch_ppocr_server_v1.1_rec_pre.tar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **预处理数据**\n",
    "* 在模型搭建和训练之前，需要做的第一件事就是对数据进行必要的预处理：\n",
    "  * 将数据处理成适合于PaddleOCR的格式，即格式为：“\" 图像文件名                    json.dumps编码的图像标注信息\"”的列表，样例如下：\n",
    "    ch4_test_images/img_61.jpg    [{\"transcription\": \"MASA\", \"points\": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]\n",
    "  * 切分训练数据为训练集和验证集，方便模型验证，切分比例为3：2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **配置文件**\n",
    "* 和其他Paddle套件一样，PaddleOCR可以通过配置文件方便的对模型的各个参数进行快速的配置\n",
    "* 自带的配置文件存放在PaddleOCR/configs目录下\n",
    "* 一般通过修改自带的配置文件就能快速的完成任务的迁移\n",
    "* 下面就通过本次使用的DB模型的配置文件来介绍一下具体设置：\n",
    "* 很多详细介绍请参考官方介绍文档，[链接](https://gitee.com/paddlepaddle/PaddleOCR/blob/develop/doc/doc_ch/config.md)\n",
    "* 下面是我的训练参数设置\n",
    "```\n",
    "Global:\n",
    "  algorithm: DB\n",
    "  use_gpu: True \n",
    "  epoch_num: 1200\n",
    "  log_smooth_window: 20\n",
    "  print_batch_step: 2\n",
    "  save_model_dir: ./output/mytrain_2_14/\n",
    "  save_epoch_step: 300\n",
    "  eval_batch_step: [0, 1000]\n",
    "  train_batch_size_per_card: 16\n",
    "  test_batch_size_per_card: 8\n",
    "  image_shape: [3, 640, 640]\n",
    "  reader_yml: ./configs/det/det_r18_myreader.yml\n",
    "  pretrain_weights: ./pretrain_models/ResNet18_vd_pretrained/\n",
    "  save_res_path: ./output/mytrain_2_14/predicts_db.txt\n",
    "  checkpoints: ./output/mytrain_2_14/best_accuracy\n",
    "  save_inference_dir:\n",
    "  infer_img:\n",
    "  pretrained_model: \n",
    "  load_static_weights:\n",
    "\n",
    "Architecture:\n",
    "  function: ppocr.modeling.architectures.det_model,DetModel\n",
    "\n",
    "Backbone:\n",
    "  function: ppocr.modeling.backbones.det_resnet_vd,ResNet\n",
    "  layers: 18\n",
    "\n",
    "Head:\n",
    "  function: ppocr.modeling.heads.det_db_head,DBHead\n",
    "  model_name: large\n",
    "  k: 50\n",
    "  inner_channels: 256\n",
    "  out_channels: 2\n",
    "\n",
    "Loss:\n",
    "  function: ppocr.modeling.losses.det_db_loss,DBLoss\n",
    "  balance_loss: true\n",
    "  main_loss_type: DiceLoss\n",
    "  alpha: 5\n",
    "  beta: 10\n",
    "  ohem_ratio: 3\n",
    "\n",
    "Optimizer:\n",
    "  function: ppocr.optimizer,AdamDecay\n",
    "  base_lr: 0.0001\n",
    "  beta1: 0.9\n",
    "  beta2: 0.999\n",
    "  decay:                                                                                                                         \n",
    "    function: cosine_decay_warmup\n",
    "    step_each_epoch: 32                                                                                                         \n",
    "    total_epoch: 1200\n",
    "\n",
    "PostProcess:\n",
    "  function: ppocr.postprocess.db_postprocess,DBPostProcess\n",
    "  thresh: 0.3\n",
    "  box_thresh: 0.6\n",
    "  max_candidates: 1000\n",
    "  unclip_ratio: 1.5\n",
    "  \n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **四、模型介绍**\n",
    ">在债券图表数据 OCR 检测与文本识别业务系统中，使用基\n",
    "于分割的 DB（Differentiable Binarization）作为基于分 割网\n",
    "络 的 文 本 检 测 器 。 首 先 ， 图 片 通 过 特 征 金 字 塔 结 构 的\n",
    "Resnet50-vd 层（Backbone 特征提取），通过上采样的方式将\n",
    "特征金字塔的输出变换为同一尺寸，并级联（Cascade）产生\n",
    "特征及特征层；然后，通过特征层预测概率图（Probability\n",
    "map）及文本概率图，用于计算该像素属于文本的概率形成\n",
    "文本概率图，然后根据各像素动态阈值形成动态阈值图；\n",
    "最后，通过文本概率图和动态阈值图生成 DB二值图，根 据 DB二值图拓展标签生成，形成文本框。\n",
    "* 模型架构图如下：\n",
    "\n",
    "![](https://bochuang.web.cloudendpoint.cn/img/lunwen3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **模型训练**\r\n",
    "* 通过下面的命令即可开启训练\r\n",
    "* -c 配置文件\r\n",
    "* Global.checkpoints 模型检查点文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "2021-02-28 23:02:28,001-INFO: {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 1200, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/mytrain_2_14/', 'save_epoch_step': 300, 'eval_batch_step': [0, 1000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 8, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_r18_myreader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/mytrain_2_14/predicts_db.txt', 'checkpoints': './output/mytrain_2_11/best_accuracy', 'save_inference_dir': None, 'infer_img': None, 'pretrained_model': None, 'load_static_weights': None}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.0001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.6, 'max_candidates': 1000, 'unclip_ratio': 1.5}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 8, 'img_set_dir': './train_data/mytrain_img/', 'label_file_path': './train_data/mytrain_img/mytrain_img_Label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/heads/det_db_head.py:123\n",
      "The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:46\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:47\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:100\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:103\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:113\n",
      "The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:113\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "2021-02-28 23:02:28,316-INFO: If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. The Regularization[L2Decay, regularization_coeff=0.000000] in Optimizer will not take effect, and it will only be applied to other Parameters!\n",
      "3 640 640\n",
      "./train_data/mytrain_img/mytrain_img_Label.txt\n",
      "2021-02-28 23:02:29,059-INFO: places would be ommited when DataLoader is not iterable\n",
      "W0228 23:02:29.094758  2558 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W0228 23:02:29.100039  2558 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-28 23:02:33,095-INFO: Finish initing model from ./output/mytrain_2_11/best_accuracy\n",
      "2021-02-28 23:02:33,095-INFO: During the training process, after the 0th iteration, an evaluation is run every 1000 iterations\n",
      "^C\n",
      "pid 2558 terminated, terminate group 2557...\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR\r\n",
    "!python tools/train.py \\\r\n",
    "    -c configs/det/det_r18_vd_db_v1.1.yml \\\r\n",
    "    -o Global.checkpoints=./output/mytrain_2_11/best_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **模型评估**\n",
    "* 通过下面的命令即可进行模型评估\n",
    "* -c 配置文件\n",
    "* Global.checkpoints 模型检查点文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-15 00:07:13,432-INFO: {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 1200, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/mytrain_2_14/', 'save_epoch_step': 300, 'eval_batch_step': [0, 1000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 8, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_r18_myreader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/mytrain_2_14/predicts_db.txt', 'checkpoints': './output/mytrain_2_14/best_accuracy', 'save_inference_dir': None, 'infer_img': None, 'pretrained_model': None, 'load_static_weights': None}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.0001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.6, 'max_candidates': 1000, 'unclip_ratio': 1.6}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 8, 'img_set_dir': './train_data/mytrain_img/', 'label_file_path': './train_data/mytrain_img/mytrain_img_Label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "W0215 00:07:13.589802 15708 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0215 00:07:13.594605 15708 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-15 00:07:18,319-INFO: Finish initing model from ./output/mytrain_2_14/best_accuracy\n",
      "2021-02-15 00:07:18,961-INFO: test tackling num:8\n",
      "2021-02-15 00:07:19,625-INFO: test tackling num:10\n",
      "2021-02-15 00:07:28,175-INFO: Eval result: {'precision': 0.9191246431969553, 'recall': 0.9378640776699029, 'hmean': 0.928399807784719}\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/eval.py -c configs/det/det_r18_vd_db_v1.1.yml  -o Global.checkpoints=\"./output/mytrain_2_14/best_accuracy\" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **模型预测**\n",
    "* 通过下面的命令即可对测试集图像进行预测\n",
    "* -c 配置文件\n",
    "* Global.checkpoints 模型检查点文件\n",
    "* Global.infer_img 预测图片路径，可以为图像文件或者图像目录\n",
    "### 可以看到我训练的模型准确度高达92%，效果怎样呢？我们通过模型推理来看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  def convert_to_list(value, n, name, dtype=np.int):\n",
      "2022-02-20 15:48:51,335 - INFO - {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': False, 'epoch_num': 1200, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/mytrain_2_14/', 'save_epoch_step': 300, 'eval_batch_step': [0, 1000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 8, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_r18_myreader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/mytrain_2_14/predicts_db.txt', 'checkpoints': './output/mytrain_2_14/best_accuracy', 'save_inference_dir': None, 'infer_img': './train_data/schoolcard.jpg', 'pretrained_model': './output/mytrain_2_14/best_accuracy', 'load_static_weights': False}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.0001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.6, 'max_candidates': 1000, 'unclip_ratio': 1.5}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 8, 'img_set_dir': './train_data/mytrain_img/', 'label_file_path': './train_data/mytrain_img/mytrain_img_Label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "2022-02-20 15:48:52,315 - INFO - Finish initing model from ./output/mytrain_2_14/best_accuracy\n",
      "2022-02-20 15:48:52,854 - INFO - tackling_num:1\n",
      "2022-02-20 15:49:10,555 - INFO - The detected Image saved in ./output/mytrain_2_14/det_results/schoolcard.jpg\n",
      "./output/mytrain_2_14/det_results/./train_data/schoolcard.jpg\n",
      "Traceback (most recent call last):\n",
      "  File \"tools/myinfer_det.py\", line 192, in <module>\n",
      "    main()\n",
      "  File \"tools/myinfer_det.py\", line 175, in main\n",
      "    txt = open(img_path[:-3]+'txt',mode='a')\n",
      "FileNotFoundError: [Errno 2] No such file or directory: './output/mytrain_2_14/det_results/./train_data/schoolcard.txt'\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR/\r\n",
    "!python3 tools/myinfer_det.py -c configs/det/det_r18_vd_db_v1.1.yml -o Global.infer_img=\"./train_data/schoolcard.jpg\" Global.pretrained_model=\"./output/mytrain_2_14/best_accuracy\" Global.load_static_weights=false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'output/mytrain_2_14/det_results/'\n",
      "/home/aistudio/PaddleOCR/output/mytrain_2_14/det_results\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdc3fb77ed0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%cd output/mytrain_2_14/det_results/\r\n",
    "import os\r\n",
    "import cv2\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\r\n",
    "img = cv2.imread('schoolcard.jpg')\r\n",
    "plt.imshow(img[:, :, [2, 1, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 训练模型转推理模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-09 11:43:24,862-INFO: {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 50, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/det_r_18_vd_db/', 'save_epoch_step': 25, 'eval_batch_step': [3000, 2000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 16, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_db_icdar15_reader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/det_r18_vd_db/predicts_db.txt', 'checkpoints': None, 'save_inference_dir': './inference/det_r18_vd_db', 'infer_img': None, 'pretrained_model': './det_r_18_vd_db/best_accuracy', 'load_static_weights': False}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.5, 'max_candidates': 1000, 'unclip_ratio': 1.6}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 2, 'img_set_dir': './train_data/', 'label_file_path': './train_data/train_icdar2019_label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "W0209 11:43:25.025346  2720 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W0209 11:43:25.029762  2720 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-09 11:43:27,861-INFO: Loading parameters from ./pretrain_models/ResNet18_vd_pretrained/...\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/io.py:2134: RuntimeWarning: Failed to load model/variables `['best_accuracy.pdparams']`, please make sure model/variables file is saved with the following APIs: save_params, save_persistables, save_vars.\n",
      "  warnings.warn(error_str % filenames, RuntimeWarning)\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/io.py:2134: RuntimeWarning: Failed to load model/variables `['best_accuracy.pdopt']`, please make sure model/variables file is saved with the following APIs: save_params, save_persistables, save_vars.\n",
      "  warnings.warn(error_str % filenames, RuntimeWarning)\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/io.py:2134: RuntimeWarning: Failed to load model/variables `['best_accuracy.pdmodel']`, please make sure model/variables file is saved with the following APIs: save_params, save_persistables, save_vars.\n",
      "  warnings.warn(error_str % filenames, RuntimeWarning)\n",
      "2021-02-09 11:43:27,870-INFO: Finish initing model from ./pretrain_models/ResNet18_vd_pretrained/\n",
      "inference model saved in ./inference/det_r18_vd_db/model and ./inference/det_r18_vd_db/params\n",
      "save success, output_name_list: ['maps']\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/export_model.py -c configs/det/det_r18_vd_db_v1.1.yml -o Global.pretrained_model=./det_r_18_vd_db/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_r18_vd_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 推理模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W0209 11:41:20.418347  2572 analysis_predictor.cc:1059] Deprecated. Please use CreatePredictor instead.\n",
      "2021-02-09 11:41:23,519-INFO: The predicted time of img: ./doc/imgs_en/img_10.jpg is  1.8358261585235596:\n",
      "2021-02-09 11:41:23,546-INFO: The visualized img saved in ./inference_results/det_res_img_10.jpg\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/infer/predict_det.py --image_dir=\"./doc/imgs_en/img_10.jpg\" --det_model_dir=\"./inference/det_r18_vd_db/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "Traceback (most recent call last):\n",
      "  File \"tools/export_model.py\", line 77, in <module>\n",
      "    main()\n",
      "  File \"tools/export_model.py\", line 49, in main\n",
      "    startup_prog, eval_program, place, config, _ = program.preprocess()\n",
      "  File \"/home/aistudio/PaddleOCR/tools/program.py\", line 655, in preprocess\n",
      "    merge_config(FLAGS.opt)\n",
      "  File \"/home/aistudio/PaddleOCR/tools/program.py\", line 127, in merge_config\n",
      "    sub_key, cur)\n",
      "AssertionError: key pretrained_model not in sub_keys: {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 50, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/det_r_18_vd_db/', 'save_epoch_step': 25, 'eval_batch_step': [3000, 2000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 16, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_db_icdar15_reader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/det_r18_vd_db/predicts_db.txt', 'checkpoints': None, 'save_inference_dir': None, 'infer_img': None}, please check your running command.\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR\r\n",
    "!python tools/export_model.py -c configs/det/det_r18_vd_db_v1.1.yml -o Global.pretrained_model=./output/det_r_18_vd_db/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_r18_db/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-09 13:40:55,987-INFO: {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 500, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/det_r_18_vd_db/', 'save_epoch_step': 100, 'eval_batch_step': [3000, 2000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 16, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_db_icdar15_reader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/det_r18_vd_db/predicts_db.txt', 'checkpoints': './output/det_r_18_vd_db/best_accuracy', 'save_inference_dir': None, 'infer_img': None, 'pretrained_model': None, 'load_static_weights': None}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.5, 'max_candidates': 1000, 'unclip_ratio': 1.6}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 1, 'img_set_dir': './train_data/mytrain/', 'label_file_path': './train_data/mytrain/train_label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/heads/det_db_head.py:123\n",
      "The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:46\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:47\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:100\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:103\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:113\n",
      "The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_basic_loss.py:113\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "2021-02-09 13:40:56,346-INFO: If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. The Regularization[L2Decay, regularization_coeff=0.000000] in Optimizer will not take effect, and it will only be applied to other Parameters!\n",
      "3 640 640\n",
      "./train_data/mytrain/train_label.txt\n",
      "2021-02-09 13:40:57,146-INFO: places would be ommited when DataLoader is not iterable\n",
      "W0209 13:40:57.188028  6872 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W0209 13:40:57.192502  6872 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-09 13:41:01,120-INFO: Finish initing model from ./output/det_r_18_vd_db/best_accuracy\n",
      "2021-02-09 13:41:01,121-INFO: During the training process, after the 3000th iteration, an evaluation is run every 2000 iterations\n",
      "2021-02-09 13:41:12,321-INFO: epoch: 0, iter: 2, 'lr': 0.000863, 'total_loss': 3.364208, 'loss_shrink_maps': 2.110946, 'loss_threshold_maps': 0.832613, 'loss_binary_maps': 0.420648, time: 3.324\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/train.py -c configs/det/det_r18_vd_db_v1.1.yml -o Global.checkpoints=./output/det_r_18_vd_db/best_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "2021-02-09 00:39:13,428-INFO: {'Global': {'debug': False, 'algorithm': 'EAST', 'use_gpu': True, 'epoch_num': 100000, 'log_smooth_window': 20, 'print_batch_step': 5, 'save_model_dir': './output/det_east/', 'save_epoch_step': 200, 'eval_batch_step': [5000, 5000], 'train_batch_size_per_card': 8, 'test_batch_size_per_card': 16, 'image_shape': [3, 512, 512], 'reader_yml': './configs/det/det_east_icdar15_reader.yml', 'pretrain_weights': './pretrain_models/ResNet50_vd_ssld_pretrained/', 'save_res_path': './output/det_east/predicts_east.txt', 'checkpoints': None, 'save_inference_dir': None, 'infer_img': None}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 50}, 'Head': {'function': 'ppocr.modeling.heads.det_east_head,EASTHead', 'model_name': 'large'}, 'Loss': {'function': 'ppocr.modeling.losses.det_east_loss,EASTLoss'}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.001, 'beta1': 0.9, 'beta2': 0.999}, 'PostProcess': {'function': 'ppocr.postprocess.east_postprocess,EASTPostPocess', 'score_thresh': 0.8, 'cover_thresh': 0.1, 'nms_thresh': 0.2}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.east_process,EASTProcessTrain', 'num_workers': 8, 'img_set_dir': './train_data/', 'label_file_path': './train_data/train_icdar2019_label.txt', 'background_ratio': 0.125, 'min_crop_side_ratio': 0.1, 'min_text_size': 10}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.east_process,EASTProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt'}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.east_process,EASTProcessTest', 'img_set_dir': './train_data/', 'label_file_path': './train_data/test_Label.txt', 'do_eval': True}}\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:37\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:38\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:39\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:48\n",
      "The behavior of expression A - B has been unified with elementwise_sub(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_sub(X, Y, axis=0) instead of A - B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:52\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:53\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:53\n",
      "The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:54\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:55\n",
      "The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_add(X, Y, axis=0) instead of A + B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:298: UserWarning: /home/aistudio/PaddleOCR/ppocr/modeling/losses/det_east_loss.py:56\n",
      "The behavior of expression A * B has been unified with elementwise_mul(X, Y, axis=-1) from Paddle 2.0. If your code works well in the older versions but crashes in this version, try to use elementwise_mul(X, Y, axis=0) instead of A * B. This transitional warning will be dropped in the future.\n",
      "  op_type, op_type, EXPRESSION_MAP[method_name]))\n",
      "./train_data/train_icdar2019_label.txt\n",
      "2021-02-09 00:39:15,732-INFO: places would be ommited when DataLoader is not iterable\n",
      "W0209 00:39:15.824185  2842 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0209 00:39:15.829442  2842 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-09 00:39:20,777-INFO: Loading parameters from ./pretrain_models/ResNet50_vd_ssld_pretrained/...\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/io.py:2284: UserWarning: This list is not set, Because of Paramerter not found in program. There are: fc_0.b_0 fc_0.w_0\n",
      "  format(\" \".join(unused_para_list)))\n",
      "2021-02-09 00:39:21,195-INFO: Finish initing model from ./pretrain_models/ResNet50_vd_ssld_pretrained/\n",
      "2021-02-09 00:39:21,196-INFO: During the training process, after the 5000th iteration, an evaluation is run every 5000 iterations\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py:1288: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n",
      "  logging.warn('Your reader has raised an exception!')\n",
      "2021-02-09 00:39:22,508-WARNING: Your reader has raised an exception!\n",
      "Exception in thread Thread-1:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/threading.py\", line 870, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 1289, in __thread_main__\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 1269, in __thread_main__\n",
      "    for tensors in self._tensor_reader():\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 1348, in __tensor_reader_impl__\n",
      "    for slots in paddle_reader():\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/data_feeder.py\", line 550, in __reader_creator__\n",
      "    for item in reader():\n",
      "  File \"/home/aistudio/PaddleOCR/ppocr/data/det/dataset_traversal.py\", line 115, in batch_iter_reader\n",
      "    for outs in sample_iter_reader():\n",
      "  File \"/home/aistudio/PaddleOCR/ppocr/data/det/dataset_traversal.py\", line 57, in sample_iter_reader\n",
      "    outs = self.process(label_infor)\n",
      "  File \"/home/aistudio/PaddleOCR/ppocr/data/det/east_process.py\", line 418, in __call__\n",
      "    infor = self.convert_label_infor(label_infor)\n",
      "  File \"/home/aistudio/PaddleOCR/ppocr/data/det/east_process.py\", line 70, in convert_label_infor\n",
      "    wordBBs = np.array(wordBBs, dtype=np.float32)\n",
      "ValueError: setting an array element with a sequence.\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"tools/train.py\", line 135, in <module>\n",
      "    main()\n",
      "  File \"tools/train.py\", line 104, in main\n",
      "    program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)\n",
      "  File \"/home/aistudio/PaddleOCR/tools/program.py\", line 302, in train_eval_det_run\n",
      "    return_numpy=False)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\", line 1110, in run\n",
      "    six.reraise(*sys.exc_info())\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/six.py\", line 703, in reraise\n",
      "    raise value\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\", line 1108, in run\n",
      "    return_merged=return_merged)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\", line 1251, in _run_impl\n",
      "    return_merged=return_merged)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/executor.py\", line 913, in _run_parallel\n",
      "    tensors = exe.run(fetch_var_names, return_merged)._move_to_list()\n",
      "SystemError: In user code:\n",
      "\n",
      "    File \"tools/train.py\", line 135, in <module>\n",
      "      main()\n",
      "    File \"tools/train.py\", line 52, in main\n",
      "      config, train_program, startup_program, mode='train')\n",
      "    File \"/home/aistudio/PaddleOCR/tools/program.py\", line 175, in build\n",
      "      dataloader, outputs = model(mode=mode)\n",
      "    File \"/home/aistudio/PaddleOCR/ppocr/modeling/architectures/det_model.py\", line 138, in __call__\n",
      "      image, labels, loader = self.create_feed(mode)\n",
      "    File \"/home/aistudio/PaddleOCR/ppocr/modeling/architectures/det_model.py\", line 125, in create_feed\n",
      "      iterable=False)\n",
      "    File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 749, in from_generator\n",
      "      iterable, return_list, drop_last)\n",
      "    File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 1119, in __init__\n",
      "      self._init_non_iterable()\n",
      "    File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/reader.py\", line 1221, in _init_non_iterable\n",
      "      attrs={'drop_last': self._drop_last})\n",
      "    File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py\", line 3018, in append_op\n",
      "      attrs=kwargs.get(\"attrs\", None))\n",
      "    File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py\", line 2102, in __init__\n",
      "      for frame in traceback.extract_stack():\n",
      "\n",
      "    FatalError: Blocking queue is killed because the data reader raises an exception.\n",
      "      [Hint: Expected killed_ != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158)\n",
      "      [operator < read > error]\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR\r\n",
    "!python3 tools/train.py -c configs/det/det_r50_vd_east.yml \\\r\n",
    "     -o Global.pretrain_weights=./pretrain_models/ResNet50_vd_ssld_pretrained/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 转为推理模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-15 00:25:50,062-INFO: {'Global': {'debug': False, 'algorithm': 'DB', 'use_gpu': True, 'epoch_num': 1200, 'log_smooth_window': 20, 'print_batch_step': 2, 'save_model_dir': './output/mytrain_2_14/', 'save_epoch_step': 300, 'eval_batch_step': [0, 1000], 'train_batch_size_per_card': 16, 'test_batch_size_per_card': 8, 'image_shape': [3, 640, 640], 'reader_yml': './configs/det/det_r18_myreader.yml', 'pretrain_weights': './pretrain_models/ResNet18_vd_pretrained/', 'save_res_path': './output/mytrain_2_14/predicts_db.txt', 'checkpoints': './output/mytrain_2_14/best_accuracy', 'save_inference_dir': './inference/mytrain_2_14/', 'infer_img': None, 'pretrained_model': './output/mytrain_2_14/best_accuracy', 'load_static_weights': False}, 'Architecture': {'function': 'ppocr.modeling.architectures.det_model,DetModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.det_resnet_vd,ResNet', 'layers': 18}, 'Head': {'function': 'ppocr.modeling.heads.det_db_head,DBHead', 'model_name': 'large', 'k': 50, 'inner_channels': 256, 'out_channels': 2}, 'Loss': {'function': 'ppocr.modeling.losses.det_db_loss,DBLoss', 'balance_loss': True, 'main_loss_type': 'DiceLoss', 'alpha': 5, 'beta': 10, 'ohem_ratio': 3}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 0.0001, 'beta1': 0.9, 'beta2': 0.999, 'decay': {'function': 'cosine_decay_warmup', 'step_each_epoch': 32, 'total_epoch': 1200}}, 'PostProcess': {'function': 'ppocr.postprocess.db_postprocess,DBPostProcess', 'thresh': 0.3, 'box_thresh': 0.6, 'max_candidates': 1000, 'unclip_ratio': 1.5}, 'TrainReader': {'reader_function': 'ppocr.data.det.dataset_traversal,TrainReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTrain', 'num_workers': 8, 'img_set_dir': './train_data/mytrain_img/', 'label_file_path': './train_data/mytrain_img/mytrain_img_Label.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'test_image_shape': [736, 1280]}, 'TestReader': {'reader_function': 'ppocr.data.det.dataset_traversal,EvalTestReader', 'process_function': 'ppocr.data.det.db_process,DBProcessTest', 'img_set_dir': './train_data/test_img/', 'label_file_path': './train_data/test_img/test_th_label.txt', 'do_eval': True}}\n",
      "3 640 640\n",
      "W0215 00:25:50.232578 17018 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0215 00:25:50.237721 17018 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-15 00:25:55,034-INFO: Finish initing model from ./output/mytrain_2_14/best_accuracy\n",
      "inference model saved in ./inference/mytrain_2_14//model and ./inference/mytrain_2_14//params\n",
      "save success, output_name_list: ['maps']\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/export_model.py -c configs/det/det_r18_vd_db_v1.1.yml -o Global.pretrained_model=./output/mytrain_2_14/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/mytrain_2_14/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 由于将epoch_num改为120后使用单卡GPU训练很费时间，同时训练具有随机性不能保证训练后与我得到同样的结果，所以我将训练120epoch后生成的模型文件打包在data/data50975/文件夹中只需要将 rec_CRNN_aug_341.zip文件解压（数据集地址：https://aistudio.baidu.com/aistudio/datasetdetail/50975），将解压后的文件替换work/PaddleOCR/output/rec_CRNN_aug_341文件夹中的文件即可跳过下面的训练步骤，可以直接进行预测。替换程序如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio\n",
      "Archive:  rec_CRNN_aug_341.zip\n",
      "replace rec_CRNN_aug_341/best_accuracy.pdmodel? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n",
      "/home/aistudio/PaddleOCR/output/rec_CRNN_aug_341\n",
      "rm: cannot remove '*.pdmodel': No such file or directory\n",
      "rm: cannot remove '*.pdopt': No such file or directory\n",
      "rm: cannot remove '*.pdparams': No such file or directory\n",
      "/home/aistudio\n",
      "['latest.pdmodel', 'best_accuracy.pdopt', 'latest.pdopt', 'best_accuracy.pdmodel', 'latest.pdparams', 'best_accuracy.pdparams']\n",
      "/home/aistudio/PaddleOCR\n"
     ]
    }
   ],
   "source": [
    "%cd ~\r\n",
    "import os\r\n",
    "import shutil\r\n",
    "!cd data/data50975 && unzip rec_CRNN_aug_341.zip\r\n",
    "%cd ~/PaddleOCR/output/rec_CRNN_aug_341\r\n",
    "!rm -r *.pdmodel\r\n",
    "!rm -r *.pdopt\r\n",
    "!rm -r *.pdparams\r\n",
    "%cd ~\r\n",
    "filelist = os.listdir('data/data50975/rec_CRNN_aug_341')\r\n",
    "print(filelist)\r\n",
    "for file in filelist:\r\n",
    "    src = os.path.join('data/data50975/rec_CRNN_aug_341', file)\r\n",
    "    dst = os.path.join('PaddleOCR/output/rec_CRNN_aug_341', file)\r\n",
    "    shutil.move(src, dst)\r\n",
    "os.chdir('/home/aistudio/PaddleOCR/')\r\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 基于CTC的推理模型转换-训练模型转推理模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-25 23:12:28,230-INFO: {'Global': {'debug': False, 'algorithm': 'CRNN', 'use_gpu': True, 'epoch_num': 120, 'log_smooth_window': 20, 'print_batch_step': 100, 'save_model_dir': 'output/rec_CRNN_aug_341', 'save_epoch_step': 1, 'eval_batch_step': 1800, 'train_batch_size_per_card': 256, 'test_batch_size_per_card': 128, 'image_shape': [3, 32, 256], 'max_text_length': 64, 'character_type': 'ch', 'loss_type': 'ctc', 'reader_yml': './configs/rec/rec_icdar15_reader.yml', 'pretrain_weights': '/home/aistudio/PaddleOCR/model/latest', 'checkpoints': './output/rec_CRNN_aug_341/best_accuracy', 'save_inference_dir': './inference/starnet', 'character_dict_path': '/home/aistudio/PaddleOCR/dict.txt', 'infer_img': None}, 'Architecture': {'function': 'ppocr.modeling.architectures.rec_model,RecModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.rec_resnet_vd,ResNet', 'layers': 34}, 'Head': {'function': 'ppocr.modeling.heads.rec_ctc_head,CTCPredict', 'encoder_type': 'rnn', 'SeqRNN': {'hidden_size': 256}}, 'Loss': {'function': 'ppocr.modeling.losses.rec_ctc_loss,CTCLoss'}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 1e-05, 'beta1': 0.9, 'beta2': 0.999}, 'TrainReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader', 'num_workers': 8, 'img_set_dir': './train_data/ic15_data', 'label_file_path': './train_data/ic15_data/rec_gt_train.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader', 'img_set_dir': './train_data/ic15_data', 'label_file_path': './train_data/ic15_data/rec_gt_test.txt'}, 'TestReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader', 'infer_img': None}}\n",
      "W0225 23:12:28.477304   884 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0225 23:12:28.482715   884 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-25 23:12:34,632-INFO: Finish initing model from ./output/rec_CRNN_aug_341/best_accuracy\n",
      "inference model saved in ./inference/starnet/model and ./inference/starnet/params\n",
      "save success, output_name_list: ['decoded_out', 'predicts']\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/export_model.py -c configs/rec/mytrain_rec_ctc.yml -o Global.checkpoints=\"./output/rec_CRNN_aug_341/best_accuracy\" Global.save_inference_dir=\"./inference/starnet\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# CRNN模型推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W0225 23:20:15.717190  1293 analysis_predictor.cc:1059] Deprecated. Please use CreatePredictor instead.\n",
      "Predicts of ./demo/28.jpg:['疡跷疡又疡讲疡焕疡来疡窿疡青疡', 0.9406951]\n",
      "Total predict time for 1 images:2.884\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/infer/predict_rec.py --image_dir=\"./demo/28.jpg\" --rec_model_dir=\"./inference/starnet/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-25 23:29:46,364-INFO: {'Global': {'debug': False, 'algorithm': 'CRNN', 'use_gpu': True, 'epoch_num': 120, 'log_smooth_window': 20, 'print_batch_step': 100, 'save_model_dir': 'output/rec_CRNN_aug_341', 'save_epoch_step': 1, 'eval_batch_step': 1800, 'train_batch_size_per_card': 256, 'test_batch_size_per_card': 128, 'image_shape': [3, 32, 256], 'max_text_length': 64, 'character_type': 'ch', 'loss_type': 'ctc', 'reader_yml': './configs/rec/rec_icdar15_reader.yml', 'pretrain_weights': None, 'checkpoints': None, 'save_inference_dir': '/home/aistudio/test', 'character_dict_path': '/home/aistudio/PaddleOCR/dict.txt', 'infer_img': 'demo/28.jpg', 'pretrained_model': './output/rec_CRNN_aug_341/best_accuracy', 'load_static_weights': False}, 'Architecture': {'function': 'ppocr.modeling.architectures.rec_model,RecModel'}, 'Backbone': {'function': 'ppocr.modeling.backbones.rec_resnet_vd,ResNet', 'layers': 34}, 'Head': {'function': 'ppocr.modeling.heads.rec_ctc_head,CTCPredict', 'encoder_type': 'rnn', 'SeqRNN': {'hidden_size': 256}}, 'Loss': {'function': 'ppocr.modeling.losses.rec_ctc_loss,CTCLoss'}, 'Optimizer': {'function': 'ppocr.optimizer,AdamDecay', 'base_lr': 1e-05, 'beta1': 0.9, 'beta2': 0.999}, 'TrainReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader', 'num_workers': 8, 'img_set_dir': './train_data/ic15_data', 'label_file_path': './train_data/ic15_data/rec_gt_train.txt'}, 'EvalReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader', 'img_set_dir': './train_data/ic15_data', 'label_file_path': './train_data/ic15_data/rec_gt_test.txt'}, 'TestReader': {'reader_function': 'ppocr.data.rec.dataset_traversal,SimpleReader'}}\n",
      "W0225 23:29:46.615022  2018 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0225 23:29:46.620560  2018 device_context.cc:372] device: 0, cuDNN Version: 7.6.\n",
      "2021-02-25 23:29:51,558-INFO: infer_img:demo/28.jpg\n",
      "2021-02-25 23:29:51,597-INFO: \t index: [2394  434 2871  977 2508  362 3124  199]\n",
      "2021-02-25 23:29:51,597-INFO: \t word : 碎包范尽笠刀讼估\n",
      "2021-02-25 23:29:51,597-INFO: \t score: 0.0003415757673792541\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/infer_rec.py -c configs/rec/mytrain_rec_ctc.yml -o Global.pretrained_model=./output/rec_CRNN_aug_341/best_accuracy Global.load_static_weights=false Global.infer_img=demo/28.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR/inference\n",
      "--2021-05-11 09:38:07--  https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar\n",
      "Resolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.229, 182.61.200.195, 2409:8c00:6c21:10ad:0:ff:b00e:67d\n",
      "Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.229|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 94362069 (90M) [application/x-tar]\n",
      "Saving to: ‘ch_ppocr_server_v1.1_rec_infer.tar’\n",
      "\n",
      "ch_ppocr_server_v1. 100%[===================>]  89.99M  14.9MB/s    in 7.2s    \n",
      "\n",
      "2021-05-11 09:38:14 (12.5 MB/s) - ‘ch_ppocr_server_v1.1_rec_infer.tar’ saved [94362069/94362069]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR/inference/\r\n",
    "!wget https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar && tar xf ch_ppocr_server_v1.1_rec_infer.tar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleOCR\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  def convert_to_list(value, n, name, dtype=np.int):\n",
      "W0511 09:47:50.337512  4373 analysis_predictor.cc:1145] Deprecated. Please use CreatePredictor instead.\n",
      "dt_boxes num : 17, elapse : 2.6785147190093994\n",
      "cls num  : 17, elapse : 0.01770186424255371\n",
      "Traceback (most recent call last):\n",
      "  File \"tools/infer/predict_system.py\", line 181, in <module>\n",
      "    main(utility.parse_args())\n",
      "  File \"tools/infer/predict_system.py\", line 145, in main\n",
      "    dt_boxes, rec_res = text_sys(img)\n",
      "  File \"tools/infer/predict_system.py\", line 105, in __call__\n",
      "    rec_res, elapse = self.text_recognizer(img_crop_list)\n",
      "  File \"/home/aistudio/PaddleOCR/tools/infer/predict_rec.py\", line 243, in __call__\n",
      "    rec_idx_lod = self.output_tensors[0].lod()[0]\n",
      "IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "%cd ~/PaddleOCR/\r\n",
    "!python3 tools/infer/predict_system.py --image_dir=\"./train_data/demo_1/idcard_kang_1.jpg\" --det_model_dir=\"./inference/v1.1_det_infer/\"  --rec_model_dir=\"./inference/v1.1_cls_infer\" --cls_model_dir=\"./inference/v1.1_cls_infer/\" --use_angle_cls=True --use_space_char=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  def convert_to_list(value, n, name, dtype=np.int):\n",
      "W0511 10:15:05.829689  7287 analysis_predictor.cc:1145] Deprecated. Please use CreatePredictor instead.\n",
      "2021-05-11 10:15:10,969 - INFO - The predicted time of img: ./train_data/demo_1/idcard_mao1.jpg is  2.706615924835205:\n",
      "2021-05-11 10:15:11,016 - INFO - The visualized img saved in ./inference_results/det_res_idcard_mao1.jpg\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/infer/predict_det.py --image_dir=\"./train_data/demo_1/idcard_mao1.jpg\" --det_model_dir=\"./inference/v1.1_det_infer/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **总结**\n",
    ">随着深度学习技术的不断发展，采用 OCR 技术进行信\n",
    "息录入的方法已成为信息录入的重要的方法之一。实验结\n",
    "果表明，本文通过基于深度学习的技术所设计的债券图表\n",
    "OCR 的检测和识别方案，能较好地识别图像中的文字如：\n",
    "身份证、校园卡、银行卡、财务报表等，实现不同实际业务\n",
    "场景中的需求，在一定程度上提高日常应用业务场景的智\n",
    "能化处理能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# **关于作者**\n",
    "* 康雪玮\n",
    "\n",
    "* 沈阳师范大学 软件工程专业 大四在读\n",
    "\n",
    "* 感兴趣的方向为：机器视觉和强化学习等\n",
    "\n",
    "* AIstudio主页：[Toopking](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/51719)\n",
    "\n",
    "* 欢迎大家有问题留言交流学习，共同进步成长"
   ]
  }
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